Artificial Intelligence - Machine Learning

This is the Bias Variance trade off. I explain this in detail. There is no escaping the relation between Bias and Variance in Machine Learning.

Error Metrics

08/25/2020

I take you through the journey of the error metrics for both Regression and Classification. We discuss the appropriateness of use of specific error metrics in specific cases. We go through the various concepts clearly explaining the mathematical and statistical meaning behind the metrics.

Logistic Regression - Explained. This presentation comes complete with all the complicated and advanced statistical concepts explained clearly with step by step examples making them crystal clear. Includes a very famous worked out example - I guide this, step by step...designed to be really interesting...

Here I go through the most important Linear Regression Assumptions. I detail what these assumptions are and how to deal with them when these assumptions are violated. A really good document before setting out on your Data Analysis journey.

Naïve Bayes

08/16/2020

I understand this is a tough one. But I make this really simple to understand. I make the intuition simple, the logic and formulas simple - I clearly explain the algorithm, I explain the concepts clearly. This couldn't have been simpler!!

This presentation aims to explain in detail the critical concepts, techniques and approaches behind K-means clustering including identification and validation of the appropriate number of clusters.

This presentation provides an in-depth look inside a Neural Network, a perceptron, the Activation Functions, the Learning Rules, Backpropagation, Optimization, the pros and cons of a Neural Network amongst others... Very nice and thoroughly useful document.

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